Comparative Application of Model Predictive Control and Particle Swarm Optimization in Optimum Operation of a Large-Scale Water Transfer System

This research evaluates the application and performance of two methods of Model Predictive Control (MPC) and Particle Swarm Optimization (PSO) in real time control and operational management of a large-scale water transfer system. Metaheuristic approaches are computationally common but do not guarantee the global optimality of their solution. On the other hand, a main limitation for these algorithms is the inability to solve large-scale optimization problems due to curse of dimensionality. Model Predictive Control is a modern control method that has been developed for industrial problems and owes its success to easy and effective applicability of constraints on state and control variables. In this research, a multi-objective optimization problem is designed for the Zarrinehrood water transfer system, the largest water transfer line in Middle East, and is solved using MPC and PSO. Two objective functions of maintaining the safe stored water in reservoirs and reducing the fluctuations in pump stations are defined. Results show that the proposed MPC model yields high capability to satisfy all constraints while fluctuations in pump stations are decreased and the volume of stored water in reservoirs meets the lowest error in the vicinity of the safe volume. Performance of MPC in optimizing management of a large-scale water transfer systems like Zarrinehrood with spontaneity in demand sector is better than PSO. However, defining the equations required to perform MPC with respect to the metaheuristic algorithm is more complicated and needs high accuracy to correctly look for the optimal solution.